OpenRaiser/Intern-Atlas huggingface dataset:
Binary Modelling and Capacity-Approaching Coding for the IM/DD Channel
Venue: arXiv cs.IT 2023
Year: 2023

The paper provides a new perspective on peak- and average-constrained Gaussian channels. Such channels model optical wireless communication (OWC) systems which employ intensity-modulation with direct detection (IM/DD). First, the paper proposes a new, capacity-preserving vector binary channel (VBC) model, consisting of dependent binary noisy bit-pipes. Then, to simplify coding over this VBC, the paper proposes coding schemes with varying levels of complexity, building on the capacity of binary-symmetric channels (BSC) and channels with state. The achievable rates are compared to capacity and capacity bounds, showing that coding for the BSC with state over the VBC achieves rates close to capacity at moderate to high signal-to-noise ratio (SNR), whereas simpler schemes achieve lower rates at lower complexity. The presented coding schemes are realizable using capacity-achieving codes for binary-input channels, such as polar codes. Numerical results are provided to validate the theoretical results and demonstrate the applicability of the proposed schemes.
Towards Understanding the Open Source Interest in Gender-Related GitHub Projects
Venue: arXiv cs.SE 2023
Year: 2023

The open-source community uses the GitHub platform to exchange and share software applications and services of interest. This paper aims to identify the open-source community's interest in gender-related projects on GitHub. Our findings create research opportunities and identify resources by the open-source community that promote diversity, equity, and inclusion. We use data mining to identify GitHub projects that focus on gender-related topics. We apply quantitative and qualitative methodologies to examine the projects' attributes and to classify them within a gender social structure and a gender bias taxonomy. We aim to understand the open-source community's efforts and interests in gender topics through active projects. In this paper, we report on a preponderance of projects focusing on specific gender topics and identify those with a narrow focus. We examine projects focusing on gender bias and how they address this non-inclusive behaviour. Results show a propensity of GitHub projects focusing on recognising and detecting an individual's gender and a dearth of projects concentrating on the cultural expectations placed on women and men. In the gender bias domain, the projects mainly focus on occupational biases. These findings raise opportunities to address the limited focus of GitHub on gender-related topics through developing projects that mitigate exclusive behaviours.
Rethinking White-Box Watermarks on Deep Learning Models under Neural Structural Obfuscation
Venue: arXiv cs.CR 2023
Year: 2023

Copyright protection for deep neural networks (DNNs) is an urgent need for AI corporations. To trace illegally distributed model copies, DNN watermarking is an emerging technique for embedding and verifying secret identity messages in the prediction behaviors or the model internals. Sacrificing less functionality and involving more knowledge about the target DNN, the latter branch called \textit{white-box DNN watermarking} is believed to be accurate, credible and secure against most known watermark removal attacks, with emerging research efforts in both the academy and the industry. In this paper, we present the first systematic study on how the mainstream white-box DNN watermarks are commonly vulnerable to neural structural obfuscation with \textit{dummy neurons}, a group of neurons which can be added to a target model but leave the model behavior invariant. Devising a comprehensive framework to automatically generate and inject dummy neurons with high stealthiness, our novel attack intensively modifies the architecture of the target model to inhibit the success of watermark verification. With extensive evaluation, our work for the first time shows that nine published watermarking schemes require amendments to their verification procedures.
Urban Regional Function Guided Traffic Flow Prediction
Venue: arXiv cs.CV 2023
Year: 2023

The prediction of traffic flow is a challenging yet crucial problem in spatial-temporal analysis, which has recently gained increasing interest. In addition to spatial-temporal correlations, the functionality of urban areas also plays a crucial role in traffic flow prediction. However, the exploration of regional functional attributes mainly focuses on adding additional topological structures, ignoring the influence of functional attributes on regional traffic patterns. Different from the existing works, we propose a novel module named POI-MetaBlock, which utilizes the functionality of each region (represented by Point of Interest distribution) as metadata to further mine different traffic characteristics in areas with different functions. Specifically, the proposed POI-MetaBlock employs a self-attention architecture and incorporates POI and time information to generate dynamic attention parameters for each region, which enables the model to fit different traffic patterns of various areas at different times. Furthermore, our lightweight POI-MetaBlock can be easily integrated into conventional traffic flow prediction models. Extensive experiments demonstrate that our module significantly improves the performance of traffic flow prediction and outperforms state-of-the-art methods that use metadata.
DiffusionSeg: Adapting Diffusion Towards Unsupervised Object Discovery
Venue: arXiv cs.CV 2023
Year: 2023

Learning from a large corpus of data, pre-trained models have achieved impressive progress nowadays. As popular generative pre-training, diffusion models capture both low-level visual knowledge and high-level semantic relations. In this paper, we propose to exploit such knowledgeable diffusion models for mainstream discriminative tasks, i.e., unsupervised object discovery: saliency segmentation and object localization. However, the challenges exist as there is one structural difference between generative and discriminative models, which limits the direct use. Besides, the lack of explicitly labeled data significantly limits performance in unsupervised settings. To tackle these issues, we introduce DiffusionSeg, one novel synthesis-exploitation framework containing two-stage strategies. To alleviate data insufficiency, we synthesize abundant images, and propose a novel training-free AttentionCut to obtain masks in the first synthesis stage. In the second exploitation stage, to bridge the structural gap, we use the inversion technique, to map the given image back to diffusion features. These features can be directly used by downstream architectures. Extensive experiments and ablation studies demonstrate the superiority of adapting diffusion for unsupervised object discovery.
High-Dimensional Approximate Nearest Neighbor Search: with Reliable and Efficient Distance Comparison Operations
Venue: arXiv cs.DS 2023
Year: 2023

Approximate K nearest neighbor (AKNN) search is a fundamental and challenging problem. We observe that in high-dimensional space, the time consumption of nearly all AKNN algorithms is dominated by that of the distance comparison operations (DCOs). For each operation, it scans full dimensions of an object and thus, runs in linear time wrt the dimensionality. To speed it up, we propose a randomized algorithm named ADSampling which runs in logarithmic time wrt to the dimensionality for the majority of DCOs and succeeds with high probability. In addition, based on ADSampling we develop one general and two algorithm-specific techniques as plugins to enhance existing AKNN algorithms. Both theoretical and empirical studies confirm that: (1) our techniques introduce nearly no accuracy loss and (2) they consistently improve the efficiency.
More Robust Schema-Guided Dialogue State Tracking via Tree-Based Paraphrase Ranking
Venue: arXiv cs.CL 2023
Year: 2023

The schema-guided paradigm overcomes scalability issues inherent in building task-oriented dialogue (TOD) agents with static ontologies. Instead of operating on dialogue context alone, agents have access to hierarchical schemas containing task-relevant natural language descriptions. Fine-tuned language models excel at schema-guided dialogue state tracking (DST) but are sensitive to the writing style of the schemas. We explore methods for improving the robustness of DST models. We propose a framework for generating synthetic schemas which uses tree-based ranking to jointly optimise lexical diversity and semantic faithfulness. The generalisation of strong baselines is improved when augmenting their training data with prompts generated by our framework, as demonstrated by marked improvements in average joint goal accuracy (JGA) and schema sensitivity (SS) on the SGD-X benchmark.
Short: Basal-Adjust: Trend Prediction Alerts and Adjusted Basal Rates for Hyperglycemia Prevention
Venue: arXiv cs.LG 2023
Year: 2023

Significant advancements in type 1 diabetes treatment have been made in the development of state-of-the-art Artificial Pancreas Systems (APS). However, lapses currently exist in the timely treatment of unsafe blood glucose (BG) levels, especially in the case of rebound hyperglycemia. We propose a machine learning (ML) method for predictive BG scenario categorization that outputs messages alerting the patient to upcoming BG trends to allow for earlier, educated treatment. In addition to standard notifications of predicted hypoglycemia and hyperglycemia, we introduce BG scenario-specific alert messages and the preliminary steps toward precise basal suggestions for the prevention of rebound hyperglycemia. Experimental evaluation on the DCLP3 clinical dataset achieves >98% accuracy and >79% precision for predicting rebound high events for patient alerts.
Large-Scale Collaborative Writing: Technical Challenges and Recommendations
Venue: arXiv cs.HC 2023
Year: 2023

Collaborative writing is essential for teams that create documents together. Creating documents in large-scale collaborations is a challenging task that requires an efficient workflow. The design of such a workflow has received comparatively little attention. Conventional solutions such as working on a single Microsoft Word document or a shared online document are still widely used. In this paper, we propose a new workflow consisting of a combination of the lightweight markup language AsciiDoc together with the state-of-the-art version control system Git. The proposed process makes use of well-established workflows in the field of software development that have grown over decades. We present a detailed comparison of the proposed markup + Git workflow to Word and Word for the Web as the most prominent examples for conventional approaches.We argue that the proposed approach provides significant benefits regarding scalability, flexibility, and structuring of most collaborative writing tasks, both in academia and industry.
GNNFormer: A Graph-based Framework for Cytopathology Report Generation
Venue: arXiv cs.CV 2023
Year: 2023

Cytopathology report generation is a necessary step for the standardized examination of pathology images. However, manually writing detailed reports brings heavy workloads for pathologists. To improve efficiency, some existing works have studied automatic generation of cytopathology reports, mainly by applying image caption generation frameworks with visual encoders originally proposed for natural images. A common weakness of these works is that they do not explicitly model the structural information among cells, which is a key feature of pathology images and provides significant information for making diagnoses. In this paper, we propose a novel graph-based framework called GNNFormer, which seamlessly integrates graph neural network (GNN) and Transformer into the same framework, for cytopathology report generation. To the best of our knowledge, GNNFormer is the first report generation method that explicitly models the structural information among cells in pathology images. It also effectively fuses structural information among cells, fine-grained morphology features of cells and background features to generate high-quality reports. Experimental results on the NMI-WSI dataset show that GNNFormer can outperform other state-of-the-art baselines.
Finding Competence Regions in Domain Generalization
Venue: arXiv cs.LG 2023
Year: 2023

We investigate a "learning to reject" framework to address the problem of silent failures in Domain Generalization (DG), where the test distribution differs from the training distribution. Assuming a mild distribution shift, we wish to accept out-of-distribution (OOD) data from a new domain whenever a model's estimated competence foresees trustworthy responses, instead of rejecting OOD data outright. Trustworthiness is then predicted via a proxy incompetence score that is tightly linked to the performance of a classifier. We present a comprehensive experimental evaluation of existing proxy scores as incompetence scores for classification and highlight the resulting trade-offs between rejection rate and accuracy gain. For comparability with prior work, we focus on standard DG benchmarks and consider the effect of measuring incompetence via different learned representations in a closed versus an open world setting. Our results suggest that increasing incompetence scores are indeed predictive of reduced accuracy, leading to significant improvements of the average accuracy below a suitable incompetence threshold. However, the scores are not yet good enough to allow for a favorable accuracy/rejection trade-off in all tested domains. Surprisingly, our results also indicate that classifiers optimized for DG robustness do not outperform a naive Empirical Risk Minimization (ERM) baseline in the competence region, that is, where test samples elicit low incompetence scores.
Mutual Information Measure for Glass Ceiling Effect in Preferential Attachment Models
Venue: arXiv cs.SI 2023
Year: 2023

We propose a new way to measure inequalities such as the glass ceiling effect in attributed networks. Existing measures typically rely solely on node degree distribution or degree assortativity, but our approach goes beyond these measures by using mutual information (based on Shannon and more generally, Renyi entropy) between the conditional probability distributions of node attributes given node degrees of adjacent nodes. We show that this mutual information measure aligns with both the analytical structural inequality model and historical publication data, making it a reliable approach to capture the complexities of attributed networks. Specifically, we demonstrate this through an analysis of citation networks. Moreover, we propose a stochastic optimization algorithm using a parameterized conditional logit model for edge addition, which outperforms a baseline uniform distribution. By recommending links at random using this algorithm, we can mitigate the glass ceiling effect, which is a crucial tool in addressing structural inequalities in networks.
Proactive Rumor Control: When Impression Counts (Full Version)
Venue: arXiv cs.SI 2023
Year: 2023

The spread of rumors in online networks threatens public safety and results in economic losses. To overcome this problem, a lot of work studies the problem of rumor control which aims at limiting the spread of rumors. However, all previous work ignores the relationship between the influence block effect and counts of impressions on the user. In this paper, we study the problem of minimizing the spread of rumors when impression counts. Given a graph $G(V,E)$, a rumor set $R \in V$ and a budget $k$, it aims to find a protector set $P \in V \backslash R$ to minimize the spread of the rumor set $R$ under the budget $k$. Due to the impression counts, two following challenges of our problem need to be overcome: (1) our problem is NP-hard; (2) the influence block is non-submodular, which means a straightforward greedy approach is not applicable. Hence, we devise a branch-and-bound framework for this problem with a ($1-1/e-ε$) approximation ratio. To further improve the efficiency, we speed up our framework with a progressive upper bound estimation method, which achieves a ($1-1/e-ε- ρ$) approximation ratio. We conduct experiments on real-world datasets to verify the efficiency, effectiveness, and scalability of our methods.
A Simple Framework for 3D Occupancy Estimation in Autonomous Driving
Venue: arXiv cs.CV 2023
Year: 2023

The task of estimating 3D occupancy from surrounding-view images is an exciting development in the field of autonomous driving, following the success of Bird's Eye View (BEV) perception. This task provides crucial 3D attributes of the driving environment, enhancing the overall understanding and perception of the surrounding space. In this work, we present a simple framework for 3D occupancy estimation, which is a CNN-based framework designed to reveal several key factors for 3D occupancy estimation, such as network design, optimization, and evaluation. In addition, we explore the relationship between 3D occupancy estimation and other related tasks, such as monocular depth estimation and 3D reconstruction, which could advance the study of 3D perception in autonomous driving. For evaluation, we propose a simple sampling strategy to define the metric for occupancy evaluation, which is flexible for current public datasets. Moreover, we establish the benchmark in terms of the depth estimation metric, where we compare our proposed method with monocular depth estimation methods on the DDAD and Nuscenes datasets and achieve competitive performance. The relevant code will be updated in https://github.com/GANWANSHUI/SimpleOccupancy.
MassNet: A Deep Learning Approach for Body Weight Extraction from A Single Pressure Image
Venue: arXiv cs.HC 2023
Year: 2023

Body weight, as an essential physiological trait, is of considerable significance in many applications like body management, rehabilitation, and drug dosing for patient-specific treatments. Previous works on the body weight estimation task are mainly vision-based, using 2D/3D, depth, or infrared images, facing problems in illumination, occlusions, and especially privacy issues. The pressure mapping mattress is a non-invasive and privacy-preserving tool to obtain the pressure distribution image over the bed surface, which strongly correlates with the body weight of the lying person. To extract the body weight from this image, we propose a deep learning-based model, including a dual-branch network to extract the deep features and pose features respectively. A contrastive learning module is also combined with the deep-feature branch to help mine the mutual factors across different postures of every single subject. The two groups of features are then concatenated for the body weight regression task. To test the model's performance over different hardware and posture settings, we create a pressure image dataset of 10 subjects and 23 postures, using a self-made pressure-sensing bedsheet. This dataset, which is made public together with this paper, together with a public dataset, are used for the validation. The results show that our model outperforms the state-of-the-art algorithms over both 2 datasets. Our research constitutes an important step toward fully automatic weight estimation in both clinical and at-home practice. Our dataset is available for research purposes at: https://github.com/USTCWzy/MassEstimation.
Toward Super-Resolution for Appearance-Based Gaze Estimation
Venue: arXiv cs.CV 2023
Year: 2023

Gaze tracking is a valuable tool with a broad range of applications in various fields, including medicine, psychology, virtual reality, marketing, and safety. Therefore, it is essential to have gaze tracking software that is cost-efficient and high-performing. Accurately predicting gaze remains a difficult task, particularly in real-world situations where images are affected by motion blur, video compression, and noise. Super-resolution has been shown to improve image quality from a visual perspective. This work examines the usefulness of super-resolution for improving appearance-based gaze tracking. We show that not all SR models preserve the gaze direction. We propose a two-step framework based on SwinIR super-resolution model. The proposed method consistently outperforms the state-of-the-art, particularly in scenarios involving low-resolution or degraded images. Furthermore, we examine the use of super-resolution through the lens of self-supervised learning for gaze prediction. Self-supervised learning aims to learn from unlabelled data to reduce the amount of required labeled data for downstream tasks. We propose a novel architecture called SuperVision by fusing an SR backbone network to a ResNet18 (with some skip connections). The proposed SuperVision method uses 5x less labeled data and yet outperforms, by 15%, the state-of-the-art method of GazeTR which uses 100% of training data.
Visual Studio Code in Introductory Computer Science Course: An Experience Report
Venue: arXiv cs.HC 2023
Year: 2023

Involving integrated development environments (IDEs) in introductory-level (CS1) programming courses is critical. However, it is difficult for instructors to find a suitable IDE that is beginner friendly and supports strong functionality. In this paper, we report the experience of using Visual Studio Code (VS Code) in a CS1 programming course. We describe our motivation for choosing VS Code and how we introduce it to students. We create comprehensive guidance with hierarchical indexing to help students with diverse programming backgrounds. We perform an experimental evaluation of students' programming experience of using VS Code and validate the VS Code together with guidance as a promising solution for CS1 programming courses.
Remote Task-oriented Grasp Area Teaching By Non-Experts through Interactive Segmentation and Few-Shot Learning
Venue: arXiv cs.CV 2023
Year: 2023

A robot operating in unstructured environments must be able to discriminate between different grasping styles depending on the prospective manipulation task. Having a system that allows learning from remote non-expert demonstrations can very feasibly extend the cognitive skills of a robot for task-oriented grasping. We propose a novel two-step framework towards this aim. The first step involves grasp area estimation by segmentation. We receive grasp area demonstrations for a new task via interactive segmentation, and learn from these few demonstrations to estimate the required grasp area on an unseen scene for the given task. The second step is autonomous grasp estimation in the segmented region. To train the segmentation network for few-shot learning, we built a grasp area segmentation (GAS) dataset with 10089 images grouped into 1121 segmentation tasks. We benefit from an efficient meta learning algorithm for training for few-shot adaptation. Experimental evaluation showed that our method successfully detects the correct grasp area on the respective objects in unseen test scenes and effectively allows remote teaching of new grasp strategies by non-experts.